Lee, Jungwun, Ph.D.
(In some occasions, look up Lee, Jung Wun)

Employment

• July 2024 -: Assistant Professor, Department of Biostatistics at Boston University, Boston, MA, U.S.

• July 2023 - June 2024: Postdoctoral Fellow (Advisor: Dr. Sebastien Haneuse), Harvard University T.H. Chan School of Public Health, Boston, MA, U.S.

Educational History

• Sep. 2019 - May. 2023, Department of Statistics at the University of Connecticut, Ph.D Student in Statistics. (Advisor : Dr. Ofer Harel)

• Sep. 2018 - May. 2019, University of Massachusetts At Amherst, Ph.D Student in Statistics.

• Mar. 2015 - Feb. 2017, Department of Statistics at Korea University, M.S. in Statistics. (Advisor: Dr. Hwan Chung) 

• Mar. 2009 - Feb. 2015, Department of Mathematics at Korea University, B.S. in Mathematics. 

Publications (Statistical Journals)

• Lee, J. W., Sieger, M. L., Becker, J., Philips. (2025+). A latent class pattern mixture model for nonignorable nonresponses in multivariate categorical data. Accepted in Computational Statistics.

• Lee, J., McCoach, D. B., Harel, O., & Chung, H. (2025). Bayesian Multilevel Latent Class Profile Analysis: Inference and Estimation for Exploring the Diverse Pathways to Academic Proficiency. Multivariate Behavioral Research, 1–19.  Link

• Lee, J. W., & Yackel, H. D. (2024). Latent class profile model with time-dependent covariates: a study on symptom patterning of patients for head and neck cancer. Journal of Applied Statistics, 1-21. Link.

• Lee, J. W., & Harel, O. (2024). A Two-Stage Classification for Dealing with Unseen Clusters in the Testing Data. Journal of Data Science, 1-20. Link.

• Lee, J. W., & Harel, O. (2024). A latent class selection model for categorical response variables with nonignorably missing data. Statistics and Its Interface, 17(4), 635-648. Link.

• Lee, J.W., Harel, O. (2023) Incomplete clustering analysis via multiple imputation. Journal of Applied Statistics, pp.1-18. Link

• Lee, J.W., Chung, H., Jeon, S.B. (2021) Bayesian multivariate latent class profile analysis: Exploring the developmental progression of youth depression and substance use. Computational Statistics & Data Analysis 161(2021): 107261. Link 

• Lee, J.W., Lee, S. (2021) A comparative study on the unified model based multifactor dimensionality reduction methods for identifying gene-gene interactions associated with the survival phenotype. BioData Mining 14, 17. Link

• Park, M., Lee, J.W., Park, T., &  Lee, S.Y. (2020) Gene-gene interaction analysis for the survival phenotype based on the Kaplan-Meier median estimate. BioMed Research International, 2020, 5282345. Link

• Lee, J.W., Chung, H. (2020) A multivariate latent class profile analysis for longitudinal data with a latent group variable. Communications for Statistical Applications and Methods, 27, 15-35. Link

• Jeon, S.B., Lee, J.W., Anthony, J.C. & Chung, H. (2017) Latent Class Analysis for Multiple Discrete Latent Variables: A Study on the Association Between Violent Behavior and Drug-Using Behaviors. Structural Equation Modeling: A Multidisciplinary Journal, 24, 911-925. Link

• Lee, J.W., Chung, H. (2017) Latent class analysis with multiple latent group variables. Communications for Statistical Applications and Methods 24.2, 173-191. Link

Publications (Collaborative papers)

• Yackel, H.D., Xu, W., Lee, J.W., Cong, X., Salner, A., Duffy, V. B., Judge, M.P. (2024+). Human Papillomavirus Status and Oral Health Symptom Patterning During Chemoradiation Treatment and Survivorship in Patients with Head and Neck Cancer: A Pilot Retrospective Longitudinal Analysis, Accepted in Oncology Nursing Forum.

• Bellizzi, K.M., Park, C.L., Lee, J.W., Harel, O., Sanft, T., Fritzson, E., Salafia, C., Ligus, K., Gnall., K, Magin, Z.E. (2024). Physical health and function trajectories in adults with cancer: psychosocial predictors of class membership. Journal of Cancer Survivorship, 1-11. Link 

• Sieger, M. L., Becker, J., Philips, J., Lee, J.W., & Moore, T. E. (2023). Latent classes among substance-involved families in child welfare: Associations with treatment completion and reunification. Children and Youth Services Review, 150, 107010. Link

• Johnson, J., Nadeau, J., Gonzalez, N., Miller, A., Govoni, K., Reed, S., Aborn, S., Nulton, L., Moore, T. E., & Lee, J.W. (2023). 130 Investigation of age, status, season, and sex on equine parasitic load in a university program. Journal of Equine Veterinary Science, 124, 104432. Link

• Yackel, H. D., Xu, W., Lee, J.W., Cong, X., Salner, A., Duffy, V. B., & Judge, M. P. Symptom Patterning Across the Cancer Care Trajectory for Patients Receiving Chemoradiation for Head and Neck Cancer: A Retrospective Longitudinal Study Using Latent Transition Analysis. Cancer Nursing, 10-1097. Link

Invited & Contributed Presentations

• A novel trajectory model for identifying longitudinal patterns of multivariate outcome variables (Invited Session). New England Statistics Symposium, June. 2025, New Haven, CT, U.S.

• Dealing with incomplete confounders and outcomes in causal inference: multiple imputation-based approach and inference (Invited Session). Eestern North American Region annual meeting, Mar. 2025, New Orleanse, LA, U.S.

• Sensitivity analysis for nonignorable missing values in blended analysis framework (Contributed Oral Presentation). International Biometric Conference, Dec. 2024, Atlanta, GA, U.S.

• A shared-parameter model for nonignorable missing values in longitudinal data: application to physical health and function trajectories in adults with cancer (Contributed Oral Presentation). 2024 APHA Annual Meeting and Expo, Oct. 2024, American Public Health Association, Minneapolis, MN, U.S.

• Latent class analysis and its inference, expansions, and applications (Invited presentation). Data Science Seminar Series, Department of Statistics and Data Science, Pukyong National University, October 2024, Busan, South Korea.

• A latent trajectory analysis for multivariate mixed-outcome and its application to electronic health records (Invited presentation). Statistics and Data Science Seminar Series, Department of Mathematics and Statistics, Auburn University. September 2024, Auburn, AL, U.S.

• A novel trajectory analysis for multivariate mixed-outcome and its application to electronic health records (Topic-contributed session). Joint Statistical Meeting, August 2024, Portland, OR, U.S.

• Sensitivity analysis for nonignorable missing values in blended analysis framework (Contributed session). Western North American Region annual meeting, June 2024, Fort Collins, CO, U.S.

• Sensitivity analysis for nonignorable missing values in blended analysis: a study on the effect of bariatric surgery via electronic health records. (Contributed session). New England Statistics Symposium, May. 2024, Storrs, CT, U.S.

• Sensitivity analysis for nonignorable missing values in blended analysis framework: a study on the effect of bariatric surgery via electronic health records. Advancing Health through Advancing Statistics, April. 2024, Department of Statistics at University of Connecticut, Storrs, CT, U.S.

• Cluster disagreements between training and testing data (Poster presentation). Tom Ten Have Symposium, June 2023, Boston, MA, U.S.

• Cluster disagreements between training and testing data (Contributed session). New England Statistics Symposium, June 2023, Boston, MA, U.S.

• A latent class profile analysis with time-varying covariates (Poster presentation). New England Statistics Symposium, June 2023, Boston, MA, U.S.

• Incomplete cluster analysis via multiple imputation (APHS/Stata Scholar Finalist). 2022 APHA Annual Meeting and Expo, Nov. 2022, American Public Health Association, Boston, MA, U.S. 

• Latent class analysis and its application. A Special Conference Celebrating UConn Department of Statistics 60th Anniversary, Oct. 2022, Department of Statistics at the University of Connecticut, Storrs, CT, U.S.

• A Bayesian inference for a latent class selection model for categorical response variables (Poster presentation). New England Statistics Symposium, Oct. 2021, Kingston, RI, U.S.

• A multivariate latent class analysis with latent group and covariates (Poster presentation). New England Statistics Symposium, May 2019, Hartford, CT, U.S.

• A multivariate latent class analysis with latent group (Poster presentation). Korean Statistical Society conference, May 2019, Korean National Statistical Office, Chuncheon, Korea. Link

• A multivariate latent class profile analysis (Poster presentation). Korean Statistical Society conference, May 2018, Korean National Statistical Office, Busan, Korea. Link

• Deterministic annealing EM algorithm in discrete latent variable modeling. Korean Statistical Society conference, November 2016, Korean National Statistical Office, Daejeon, Korea. Link

• Model identification in joint latent class analysis (Poster presentation). International Conference on Applied Statistics, July 2016, Phuket, Thailand. Link

• The multiple latent grouped-latent class model with covariate : patterns of violence behavior under substance use among U.S. high-school students. Korean Statistical Society conference, May 2016, Kyungpook National University, Daegu, Korea. Link

Grants and Fellowships

• APHA/Stata Scholar Student Research Competition, USD-600 (2022).

• University of Connecticut Graduate School Doctoral Dissertation Fellowship, USD - 2,000 (2022).

• University of Connecticut Graduate School Conference Participation Fellowship, USD - 750 (2022).

• University of Connecticut Department of Statistics Pre-doctoral Fellowship USD - 2,200 (2022).

• University of Connecticut Department of Statistics Pre-doctoral Fellowship USD - 790 (2021).

Awards and Honors

• Mark of Excellence (Perfect scoring in the Qualifying exam), Department of Statistics, University of Connecticut (2021) Certificate.

• Excellence in Teaching (High scoring in the Student Evaluation in Teaching), Graduate School, University of Connecticut (2020) Certificate.

• Gottfried Noether Award (Best performance in Mathematical Statistics), Department of Statistics, University of Connecticut (2020).

• 3rd place prize in SG Best Student Paper Presentation Award (Poster Session), Korean Statistical Society (May. 2019).

• 1st place prize in SG Best Student Paper Presentation Award (Poster Session), Korean Statistical Society (May. 2018).

• 3rd place prize in SG Best Student Paper Presentation Award, Korean Statistical Society (Nov. 2016).

• 3rd place prize in SG Best Student Paper Presentation Award, Korean Statistical Society (May. 2016).

Teaching Experience

• Elementary Concepts of Statistics (Spring 2023, STAT1100Q, Instructor).

• Probability Models for Engineers (Fall 2022, STAT3345Q, Instructor).

• Elementary Concepts of Statistics (Summer 2022, STAT1100Q, Teaching Assistant).

• Introduction to Mathematical Statistics II (Spring 2022, STAT3445Q-004, Instructor).

• Introduction to Mathematical Statistics I (Fall 2021, STAT3375Q-004, Instructor).

• Elementary Concepts of Statistics (Summer 2021, STAT1100Q, Teaching Assistant).

• Introduction to Mathematical Statistics II (Spring 2021, STAT3445-004, Instructor).

• Introduction to Mathematical Statistics II (Spring 2021, STAT3445-004, Teaching Assistant).

• Elementary Concepts of Statistics (Spring 2021, STAT1100Q, Teaching Assistant).

• Introduction to Mathematical Statistics I (Fall 2020, STAT3375Q-002, Instructor, Certificates).

• Introduction to Mathematical Statistics I (Fall 2020, STAT3375Q-002D, Teaching Assistant).

Work and Voluntary Activities

• Graduate Assistant Consultants, Statistical Consulting Team, University of Connecticut (Sep. 2021 - June 2023).

• Data Analyst, Admissions Office of Korea University (Dec. 2017 - May 2018).

• Statistics Consultant, Korea Radio Promotion Association (Feb. 2017 - Mar. 2017).

• Voluntary Tutor, Center for Teaching and Learning in Korea University (Sep. 2014 - Dec. 2014).

Softwares

• R program for Two-stage classification

• R program for LCSM

• R program for MICA

• R program for MLCPA

• R program for UMMDR

• R program for KMMDR

• R program for GLCPA  

• R program for JLCA  

• R program for GLCA